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NEW!!! rpkg.net flagship R package "quickcode" now available in CRAN quickcode TM
REMLA
View on CRAN: Click
here
Download and install REMLA package within the R console
Install from CRAN:
install.packages("REMLA")
Install from Github:
library("remotes")
install_github("cran/REMLA")
Install by package version:
library("remotes")
install_version("REMLA", "1.1")
Attach the package and use:
library("REMLA")
Maintained by
Bryan Ortiz-Torres
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-03-26
Latest Update: 2024-03-26
Description:
Traditional latent variable models assume that the population
is homogeneous, meaning that all individuals in the population are
assumed to have the same latent structure. However, this assumption is
often violated in practice given that individuals may differ in their
age, gender, socioeconomic status, and other factors that can affect
their latent structure. The robust expectation maximization (REM)
algorithm is a statistical method for estimating the parameters of a
latent variable model in the presence of population heterogeneity as recommended by
Nieser & Cochran (2023) . The REM algorithm is based on the
expectation-maximization (EM) algorithm, but it allows for the case
when all the data are generated by the assumed data generating model.
How to cite:
Bryan Ortiz-Torres (2024). REMLA: Robust Expectation-Maximization Estimation for Latent Variable Models. R package version 1.1, https://cran.r-project.org/web/packages/REMLA
Previous versions and publish date:
1.0 (2024-03-26 10:20)
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